dispel.processing.assertions module

Assertions to be made on readings as part of processing steps.

class dispel.processing.assertions.AssertEvaluationFinished

Bases: ProcessingStep

Assertion to ensure evaluations are finished.

process_reading(reading, **kwargs)

Ensure reading evaluation is finished.

Parameters:

reading (Reading) –

Return type:

Generator[ProcessingResult | ProcessingControlResult, None, None]

class dispel.processing.assertions.AssertRawDataSetPresent

Bases: LevelProcessingStep

Assertion to ensure specific data sets are present.

Deprecated since version 0.0.51: Use assert_level_valid

__init__(data_set_id, level_filter=None)

Parameters:

• data_set_id (str) –

• level_filter (str | LevelId | List[str] | List[LevelId] | LevelFilter | None) –

static __new__(cls, *args, **kwargs)

process_level(level, reading, **kwargs)

Ensure level has data set id.

Parameters:

• level (Level) –

• reading (Reading) –

Return type:

Generator[ProcessingResult | ProcessingControlResult, None, None]

class dispel.processing.assertions.NotEmptyDataSetAssertionMixin

Bases: DataSetProcessingStepProtocol

A mixin to ensure that processed data sets are not empty.

assert_valid_data_sets(data_sets, level, reading, **kwargs)

Assert that data sets are not empty.

Parameters:

• data_sets (Sequence[DataFrame]) –

• level (Level) –

• reading (Reading) –

assertion_message = 'Empty dataset {data_set_id} for level {level}'

The assertion message

empty_data_set_handling = 'raise'

The handling if a data set is empty